Enhancing Product Quality Risk Classification

Product quality engineers at IBM spend an inordinate amount of time manually classifying the risk categories of products based on their return rates. Huge data volume, high data velocity, data veracity issues and poor quality data, together with the combination of structured and unstructured data, make this a formidable task.

An AI solution that had been implemented enabled IBM to achieve 80 percent accuracy in risk classification, but the company wanted to improve this by another 5-10 percent. And it managed to do this through a collaboration under AI Singapore’s flagship 100Experiments (100E) programme.

The 100E team comprised AI apprentices, AI engineers and project managers. Their aim was to develop an AI model that could classify the quality of hardware products more accurately as well as predict future product return rates.

Achieving over 90 percent accuracy
Using image processing, deep learning and time-series analytics, the team created an AI model that was able to achieve over 90 percent accuracy in product risk classification, and enabled them to predict the return rates of different hardware products. The engineers could thus make decisions on measures they could take to better manage the products.

With the deployment of the AI product risk classification solution in October 2019, IBM was able to reduce the training for the AI model from 4 hours to just 15 minutes with IBM PowerAI. The time and cost savings were amplified by the fact that product engineers no longer had to sort through large volumes of data manually.

Julian Tan, a senior manager at IBM Analytics Solutions, was impressed by the significant improvements in the model’s performance and the minimum viable product that was delivered. “This collaboration has helped IBM solve our business problem as well as upskill our employees in AI,” he said. “Kudos to the entire AI Singapore team for their professionalism and insights into the world of AI.”

Click here for more details on the 100E Programme.